In earlier reported works under the unsupervised category, match filter has received the enormous response of the scholars due to its straightforwardness in the implementation of the technique. Match filter based retinal vessel segmentation relies on a 2D kernel with Gaussian profile initially proposed by Chaudhuri et al. [1]}. The kernel is rotated at 15o increment, and the best output of the filter for each pixel is carefully chosen to map all blood vessels orientated at different angles. After that, thresholding is applied to get binary vessel map image. Further, pruning is applied as post-processing to improve the final identification of blood vessels. Hoover et al. [2]} have used local and region-based properties for vessel segmentation where threshold probing technique is used on match filter response. However, match filter gives a strong response in terms of vessels and non-vessels edges. Zhang et al. [3]} have exploited the first-order derivative of Gaussian to improve the performance of matched filter by eliminating non-vessel edges from retinal images. In literature [4]}[5]}, multiscale match filter and its variation are suggested to identify blood vessels of different thickness. In [6]}, Zhao et al. have enhanced the retinal vessels in retinal images by utilizing 2D-Gabor wavelet filters and a contrast-limited adaptive histogram equalization (CLAHE). After that, the processing results of region growing method and level set approach are combined to get final segmentation as a binary image. However, this approach is unable to remove non-vessel structures also takes long processing time. Roychowdhury et al. [7]} have performed an unsupervised iterative process to obtain the vessels using top-hat reconstruction followed by iterative region growing method. Most of the approaches like [3]}, [6]} fail to remove optic disc and exudates from pathological images. Also, many approaches remove these structures in postprocessing with the extra burden of computational time. Thus, an automated unsupervised blood vessel segmentation approach is needed to identify the blood vessel pixels correctly with a small false positive rate. Simultaneously, need to remove the anatomical structure other than blood vessels with high accuracy and less complexity.
